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Acquiring reliable microstructure datasets is a pivotal step toward thesystematic design of materials with the aid of integrated computationalmaterials engineering (ICME) approaches. However, obtaining three-dimensional(3D) microstructure datasets is often challenging due to high experimentalcosts or technical limitations, while acquiring two-dimensional (2D)micrographs is comparatively easier. To deal with this issue, this studyproposes a novel framework for 2D-to-3D reconstruction of microstructurescalled Micro3Diff using diffusion-based generative models (DGMs). Specifically,this approach solely requires pre-trained DGMs for the generation of 2Dsamples, and dimensionality expansion (2D-to-3D) takes place only during thegeneration process (i.e., reverse diffusion process). The proposed frameworkincorporates a new concept referred to as multi-plane denoising diffusion,which transforms noisy samples (i.e., latent variables) from different planesinto the data structure while maintaining spatial connectivity in 3D space.Furthermore, a harmonized sampling process is developed to address possibledeviations from the reverse Markov chain of DGMs during the dimensionalityexpansion. Combined, we demonstrate the feasibility of Micro3Diff inreconstructing 3D samples with connected slices that maintain morphologicallyequivalence to the original 2D images. To validate the performance ofMicro3Diff, various types of microstructures (synthetic and experimentallyobserved) are reconstructed, and the quality of the generated samples isassessed both qualitatively and quantitatively. The successful reconstructionoutcomes inspire the potential utilization of Micro3Diff in upcoming ICMEapplications while achieving a breakthrough in comprehending and manipulatingthe latent space of DGMs.

Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling